Condense
English

Most LLMs are biggerthan your problem.
Build exactly what you need, in plain English.

≥99% accuracy retained8–15× smallerruns on-device
Get started
The old way
  • Every request → a massive model.
  • Every token → a bill.
  • Every answer → average.
The new way
  • Describe your feature.
  • Get a small specialized model.
  • Run it cheaper, faster, better.

A smaller model trained for your exact task
can outperform larger models
because it only learns what matters.

What this looks like.

One example: a SaaS that auto-replies to customer support tickets.

Before: GPT-5 API

  • ~$10 per 1M output tokens
  • One generic model handling everything
  • No improvement over time
  • Your tickets shape OpenAI's models, not yours

After: A 1B model fine-tuned on your tickets

  • ~$0.50 per 1M tokens on a $0.40/hr GPU
  • Trained on your actual conversations
  • Stays sharp on your domain
  • Yours. Self-hosted. No vendor lock-in.

Use your own data, or let the AI find a public dataset for you.

Cost estimates: GPT-5 API published rate; self-hosted 1B model on a single GPU at typical throughput. Real numbers depend on your traffic.

Under the hood.

Real ML techniques. You just don't have to know them.

Distillation

Train a small student model on a large teacher's outputs. Keep the knowledge, drop the size.

Quantization

Shrink the weights from FP16 to INT4/INT8. 4–8× smaller. Runs on consumer hardware.

Pruning

Remove the weights that don't matter. Faster inference, same accuracy.

LoRA

Train a thin adapter instead of the whole model. Cheap to train, easy to swap.

Simple, Transparent Pricing

Buy tokens, run compression jobs. 1 token = 1 hour of compute.

1 token = 1 hour of compression · $7/token base price

Builder

8% off
$96.60
$6.44 / token
15tokens
H100-1-80G

Perfect for solo developers and small-scale model experiments.

Compression methods

Knowledge DistillationCoT DistillationGPTQPruningLoRA
  • 15 compression tokens
  • All compression types
  • HuggingFace integration
Most Popular

Studio

15% off
$238
$5.95 / token
40tokens
H100-1-80G

For teams running regular compression pipelines in production.

Compression methods

Knowledge DistillationCoT DistillationGPTQPruningLoRA
  • 40 compression tokens
  • All compression types
  • HuggingFace integration
  • Priority support

Scale

22% off
$546
$5.46 / token
100tokens
H100-1-80G

High-volume compression for enterprise and research teams.

Compression methods

Knowledge DistillationCoT DistillationGPTQPruningLoRA
  • 100 compression tokens
  • All compression types
  • HuggingFace integration
  • Priority support
  • Advanced benchmarking

Tokens never expire · Unused tokens roll over · Refunded on job failure

Incoming

30 Seconds to Value

Install, compress, deploy. It's that simple.

1
Install SDK
2
Initialize Client
3
Start Compression Job
4
Download Result
main.py
1from condense import Condense
2 
3client = Condense(api_key="...")
4 
5# Start compression job
6job = client.compress(
7 model="meta-llama/Llama-3-8b",
8 target_size="800M",
9 strategy="distillation"
10)
11 
12# Download result
13job.wait_until_done()
14job.download("./model")

Stay Updated.
Join the Community.

Get the latest updates on model compression research and features.

Weekly research digests
Product updates
Community access